診斷你的決策

Diagnosing Your Decision
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《哈佛商業評論》英文版執行編輯莎拉.克利夫(Sarah Cliffe)解釋,在不確定的世界中進行策略賭注時,如何知道要使用哪些決策支援工具,就像〈決定如何決策〉一文中所描述的。

我是《哈佛商業評論》英文版執行編輯莎拉.克利夫(Sarah Cliffe)。高階主管在做出高風險決策之前,首先必須思考要如何做決定。確實會出現許多流程問題,其中最重要的問題之一,是要使用哪種決策支援工具。有很多好的決策支援工具,但許多較傳統的工具,在企業面臨高度不確定性時根本沒有幫助,面對現實吧,今天很多人面臨的就是高度不確定性。

這個決策樹,呈現麥當勞高階主管可能要做出的假設性決策,它是由考尼、洛瓦羅、克拉克開發出來的,在《哈佛商業評論》2013年11月號的文章中說明。他們的方法,分解了你在面臨重大決策時必須自問的問題。它應該會引導你找到一個適當的決策支援工具,或一組工具。

決策樹取決於三個關鍵問題,我們將在這裡概述其中兩個。第一,我是否知道成功須具備哪些條件?換句話說,我是否理解成功的完整因果模型?第二,我能否預測這個決策的各種可能結果的範圍和可能性?我們會透過這些問題,逐步從很清楚明暸的情境脈絡,過渡到很不確定的情境脈絡。我們從一個簡單的決定開始。麥當勞必須決定在美國的哪裡開設新餐廳。

是否已知道完整的因果模型?

在這種情況下,麥當勞是否已知道完整的因果模型?絕對是的。該公司知道對成功很重要的變數,例如當地人口結構、人流模式、房地產價格等。此外,它擁有關於所有這些變數的絕佳資料來源,也有經過良好校準、精心測試的營收和成本模型。此外,決策結果是已知的,部分原因是它們有很好的資訊。麥當勞可預測某個位置的表現達一定精準度,因為它過去在美國開過很多家店。

在這種很熟悉的情境中,高階主管可使用標準的傳統資本預算工具,如標準的現金流折現法,來做出是或否的明確決定。假設麥當勞要決定是否在美國市場推出新的三明治。它仍有相關的人口結構、人流等資料,所以也確實知道成功的條件。

但推出三明治的結果有一些不確定性。高階主管不確定需求會有多少,也不知道這對互補產品的銷售會有什麼影響。但在美國不同地區的一些市場研究,可以解決這個問題,提供一系列可能的結果和一些可能性。然後,他們可以使用某項工具,例如蒙地卡羅模擬或實質選擇權分析。這些是量化的多情境分析工具,用於決定是否推出三明治、在何處推出。

第一次進入新興市場該怎麼辦?

現在讓我們把不確定性提高一級。如果麥當勞是第一次進入新興市場怎麼辦?高階主管仍知道成功所需條件。相關變數,例如人口統計、基礎成本、營收模式等,大致是相同的。但決策結果未知,因為這是公司第一次進入這個地區。所以,利用市場研究和統計分析來預測結果,就像對推出三明治所做的那樣,是很難的。

在這種情況下,麥當勞可以使用質性情境分析,以便更了解可能的結果。它可以建構多種情境,涵蓋各種顧客接受率、供應商成本和可靠性水準。然後,高階主管應該在這些情境之外,搭配進行在類似商業情況下的個案決策分析,也許是檢視自家公司進入其他開發中市場的結果。

如何進入新的業務領域,採取全新的商業模式?

現在,假設麥當勞希望進入新的業務領域,採取全新的商業模式,像是提供餐飲服務流程改善的諮詢服務。由於這是全新的情況,高階主管可能無法定義完整的因果模型。但他們也許可以界定可能的結果範圍,只要他們能利用正確的資訊來源,例如,研究更有經驗運用這種商業模式的公司。

在這種情況下,麥當勞的最佳做法是直接進行個案決策,找到多個類比案例,然後嚴謹地比較自身與那些參考案例的情況。對於這個難題,兩個問題的答案都是否定的。如果麥當勞必須回應美國對肥胖問題的擔憂,以及美國人對速食業造成肥胖普及現象的反彈,它該怎麼辦?

這種反彈有可能徹底改寫速食業的規則,並讓它們的歷史資料過時。因此,麥當勞真的不知道成功的樣貌,也無法預見自己可能採取的行動,會帶來哪些可能的結果,因為它無法準確預測未來的訴訟、醫學研究、競爭對手的行動,或消費者的反應。

面對如此高度的不確定性,麥當勞應再次仰賴個案決策分析。相關的比較可能包括:飲料公司試圖重新自我定位為健康或安全的,或是槍械產業試圖影響法規和立法的行動。每個攸關重大利害的決策,當然都有自身的挑戰。但這種方法可協助你做出更明智的決策,決定要使用哪種工具做決策。

若要進一步了解如何使用決策支援工具,請參考《哈佛商業評論》2013年11月號的〈決定如何決策〉。希望你會喜歡它。

(劉純佑譯)


I am Sarah Cliffe, the executive editor of the “Harvard Business Review.” Before executives make high-risk decisions, they need to think first about how they're going to decide. A lot of process questions do come up, and one of the important ones is what decision support tool to use. There are a lot of good decision support tools, but many of the more traditional ones are not helpful at all when a business faces high levels of uncertainty, which -- let's face it -- many of us do today.

This decision tree, which features hypothetical decisions that McDonald's executives might need to make, was developed by Hugh Courtney, Dan Lovallo, and Carmina Clarke, and is described in the November 2013 issue of “HBR.” Their methodology breaks down the questions you need to ask yourself when faced with a major decision. It should lead you to the right decision support tool or set of tools.

The tree relies on three key questions, two of which we'll outline here. First, do I know what it will take to succeed? In other words, do I understand my full causal model for success? And second, can I predict the range and likelihood of different possible outcomes of the decision? We're going to work our way through those questions, moving from a very straightforward context to a very uncertain one. We'll start with a simple decision. McDonald's needs to decide where to locate new US restaurants.

In this case, is the full causal model known? It absolutely is. The company knows the variables that matter for success, things like local demographics, foot traffic patterns, real estate prices, and so on. In addition, it has terrific data sources on all of those variables, and it has a well-calibrated, well-tested revenue and cost model. In addition, the decision outcomes are known, partly because they have such good information. McDonald's can predict with some precision how well a location will perform because it's opened so many US stores in the past.

In this really familiar context, executives can use standard, conventional capital budgeting tools, things like standard discounted cash flow, to make a clear go/no go decision. Now it's say McDonald's is deciding whether to introduce a new sandwich into the US market. They still have relevant data about demographics, foot traffic, and so forth. So they do know what it will take to succeed.

But there's some uncertainty about the outcome of introducing a sandwich. Executives don't know for sure what the demand will be, nor do they know what impact it will have on the sale of complementary products. However, some market research in different parts of the country can solve that problem, will give them a range of possible outcomes, and some probabilities. They can then use a tool like Monte Carlo simulation or a real options analysis. These are quantitative multiple scenario tools to make a decision about whether to introduce the sandwich and where.

Now let's ratchet the uncertainty up a notch. What if McDonald's is entering an emerging market for the first time? Executive still know what it takes to succeed. The variables, like demographics, and the basic cost and revenue models, are more or less the same. However, the decision outcomes aren't known, since this is the company's first entrance in this locale. So predicting outcomes using market research and statistical analysis, as we did with the sandwich introduction, would be difficult.

In this situation, McDonald's can use qualitative scenario analysis to get a better sense of possible outcomes. It can build scenarios covering a range of customer acceptance rates and a range of supplier costs and reliability levels. Executives should then supplement those scenarios with case-based decision analysis of similar business situations, perhaps looking at outcomes from their own entries into other developing markets.

Now let's say McDonald's wants to enter a new line of business with a completely new business model, like consulting on food service process improvements. Since this is a brand new situation, executives probably can't define their full causal model. However, they probably can to define the range of possible outcomes if they tap into the right information sources, for example, by studying companies who have more experience with this business model.

In this case, McDonald's best bet is to go straight to case-based decision making, to find multiple analogies, then develop a rigorous comparison between itself and those reference cases. Now for the doozy, where the answer to both questions is no. What if McDonald's needs to respond to concern about obesity in the US and a backlash over the fast food industry's role in that epidemic?

The backlash has the potential to fundamentally rewrite the rules for the fast food industry and to make their historical data obsolete. So McDonald's really doesn't know what success will look like, nor can the company foresee a range of possible outcomes to moves they might make, since they can't accurately forecast future lawsuits, medical research, competitor moves, or consumer reactions.

When faced with this very high level of uncertainty, McDonald should once again rely on case-based decision analysis. Relevant comparisons might include beverage companies' attempts to reposition themselves as healthy or safe, or the firearms industries efforts to influence regulation and legislation. Every high stakes decision has its own challenges, of course. But this methodology can help you make smarter decisions about what tools to use when making them.

You can learn more about how to use decision support tools in the article “Deciding How to Decide” in the November 2013 issue of “ HBR.” We hope you'll enjoy it.



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